Once disparate censorship and undertesting are identified, noisy-label and censored ML methods represent a possible direction for mitigating negative impacts [1]}, [2]}, [3]}, [4]}.
Beyond ML methods, modeling techniques in the presence of censored/missing data include inverse probability weighting-based methods in the epidemiology/causal inference literature [5]} or the Heckman correction in the quantitative social sciences [6]}.
